Using ochre to evaluate synthetic ocr post processing dataset generation about ochre

[jvdzwaan]

So, I have updated the documentation and added the workflows for calculating performance. You don't need a lot of Python knowledge, just follow the installation instructions and adjust the paths in the cwltool commands.

Let me know if you run into problems!

[jvdzwaan]

Sorry for my late reply, I also have been busy with other projects. I think ochre could be useful for you. It uses an existing tool to calculate wer and cer: https://github.com/impactcentre/ocrevalUAtion with one small change, the default limit of 10,000 characters is removed (so by default it only calculates wer and cer for files containing a maximum of 10,000 characters).

If you just want to try the ocrevalUAtion tool, have a look at https://hub.docker.com/r/nlppln/ocrevaluation-docker/

After installing docker, you can run it with:

docker run -i --rm -v=/path/to/data/:/data/ nlppln/ocrevaluation-docker java -cp /ocrevalUAtion/target/ocrevaluation.jar eu.digitisation.Main -o /data/out.html -gt /data/gs/gs-file.txt -ocr /data/ocr/ocr-file.txt

This assumes that in /path/to/data you have two folders, one called gs containing the gold standard files and one called ocr containing the ocr files. The result is that there will be a new file in /path/to/data called out.html containing (amongst other things) the wer and cer.

The workflow lets you run the tool for a directory of files, extracts the wer and cer, and puts them in a csv-file. (I see that I haven't committed the ocrevaluation workflow just yet.)

I am planning to add the workflow and more documentation, but don't know exactly when I'll have time.

Your project sounds cool and useful for the work I am doing. I'll try to update the documentation soon!

[omrishsu]

Thanks a lot!
Now I understand the folder structure, and I've updated my code to work with ochre's structure.
I'll try the workflows as soon as possible and update on my progress.

Thanks again
Omri